Spinal cord stimulation (SCS) for chronic pain management is safe and widely utilized throughout the world. It represents one of the most common neurostimulation therapies, not just for chronic pain management, but for many other indications. In the United States, it represents a market size of more than a billion dollars per year. Traditionally, SCS has been programmed based on patient-reported paresthesias, i.e., tingling sensations, induced by the electrical stimulation. Clinical observations suggest that successful pain relief with SCS requires that these paresthesias are generated over the painful areas. However, with this programming method, only approximately 50% of patients obtain sufficient pain relief. This success rate means that SCS fails in half of patients. Because SCS is typically considered a treatment for patients with refractory pain, these patients have few, if any, other options. It is also not clear if these paresthesias are necessary for pain relief or if they are just epiphenomena. Further, the most common outcome measure for pain therapies is pain ratings provided by the patient on a 0-10 or 0-100 point scale, which is a very subjective method to evaluate the clinical success of the treatment.
Another form of neurostimulation, deep brain stimulation (DBS), has been evaluated for different indications such as depression, Alzheimer's disease, Parkinson's disease, and essential tremor. Unlike programming DBS for essential tremor, where the effects of a given stimulation setting on the disease can be immediately observed (e.g., reduction in tremor), it can be much harder to program DBS for other indications, particularly when it may take several days or weeks for a given stimulation setting to produce the desired effect.
The traditional programming strategies employed with SCS and DBS each suffer from various limitations. As such, there is a need for better and more objective strategies for determining effective neurostimulation parameters for different disease states.